98 papers
This study utilized focused qualitative inquiry with cancer patients and clinicians to evaluate a proposed virtual Dialectical Behavior Therapy Skills Training intervention for smoking cessation, revealing strong enthusiasm for the program's core skills while identifying specific areas for refinement prior to a feasibility trial.
This study characterizes the longitudinal molecular and immune evolution of recurrent MN1::PATZ1 fusion-positive CNS tumors, revealing a distinct transcriptomic subtype with features of tumor maturation toward oligodendroglioma-like phenotypes and complex immune dysfunction involving T cell exhaustion and macrophage enrichment.
This study utilized network analysis on a large dataset of 50,000 pancreatic cancer patients to map symptom interdependencies across disease stages, revealing that while symptom networks become sparser over time and node predictability remains low, specific hubs like jaundice and weight loss emerge as dominant indicators that could aid in earlier clinical recognition.
This study demonstrates that the log odds of positive lymph nodes (LODDs) outperforms traditional AJCC N stage and lymph node ratio in prognostic accuracy for gastric adenocarcinoma, leading to the development of a validated nomogram for individualized survival assessment.
This study demonstrates that the prognostic impact of tumor-associated macrophages reverses across tumor histologies, with high expression of markers like FOLR2 and TREM2 predicting improved survival in melanoma but worse outcomes in lung squamous carcinoma, highlighting the critical importance of tumor-context-dependent macrophage polarization for therapeutic strategies.
This study demonstrates that a single assessment of undetectable circulating tumor DNA (ctDNA) 3–9 weeks after initiating immunotherapy serves as a robust, real-time predictor of improved progression-free and overall survival in patients with metastatic non-small cell lung cancer.
This study introduces an integrated framework combining empirical interpolation, Dual Annealing-optimized logistic modeling, and the ThresholdXpert 1.0 combinatorial optimization tool to robustly determine diagnostic cut-off points, successfully identifying optimized multimarker panels for hepatocellular carcinoma.
This study characterizes therapy-associated polyposis (TAP) as a distinct syndrome in childhood cancer survivors marked by long latency, heterogeneous histology, and a specific alkylating-agent mutational signature (SBS25) that drives extensive genomic instability and secondary malignancies, thereby distinguishing it from hereditary forms and supporting tailored surveillance strategies.
This study demonstrates that a DeepSurv deep learning model, trained on 31 tumor gene mutations, effectively stratifies survival risk heterogeneity specifically in NSCLC patients receiving nivolumab-based immunotherapy, achieving significant risk group separation (C-index 0.789) that was not observed in chemotherapy-only patients.
This study leverages the UAE's nationwide genomic data to comprehensively map inherited breast cancer risk by identifying population-specific pathogenic variants, validating polygenic risk scores, and establishing a framework for precision prevention and early detection in Arab populations.
This study demonstrates that integrating a 26-gene molecular prognostic classifier into the 9th edition TNM staging system creates a novel TNMEx model that significantly outperforms both the 8th and 9th editions in risk stratification and survival prediction for resected lung adenocarcinoma patients.
This study characterizes the genomic landscape of upper urinary tract urothelial carcinoma (UTUC) and its intravesical recurrences, identifying FGFR3 as a key therapeutic target for preventing recurrence and TERT, FGFR3, and HRAS mutations as promising noninvasive urine markers for post-surgical surveillance.
This study validates a phospho-specific antibody for TRAF1 S146 and demonstrates that both PKN1 and PKN2 constitutively phosphorylate this site in primary CLL cells, a process reversible by the inhibitor OTSSP167, thereby supporting the development of PKN1/2 inhibitors as a therapeutic strategy for CLL.
This single-center prospective pilot study demonstrates that a combined probiotic/prebiotic intervention (MBR-01) significantly reduces the incidence and severity of abemaciclib-induced diarrhea, preserves gut microbiota diversity, and improves quality of life in patients with high-risk early breast cancer.
This study utilizes AI models to analyze whole-slide digital pathology images, identifying novel stromal density-based biomarkers that effectively predict DCIS recurrence risk and could help reduce overtreatment by better stratifying patients for adjuvant therapy.
A survey of breast cancer survivors reveals that while acceptance of tamoxifen remains high, awareness of its associated uterine cancer risk is insufficient, highlighting the need for improved risk communication and the development of patient-preferred endometrial surveillance strategies.
By integrating differential gene expression analysis with interpretable XGBoost machine learning on TCGA pancreatic tissue data, this study identified a robust molecular signature featuring genes like GJB3, LINC02086, and TSPAN1 that achieves high diagnostic accuracy (AUC 0.9868) for pancreatic ductal adenocarcinoma.
This multicohort analysis reveals that while BRCA1/2 zygosity status serves as a significant prognostic factor in metastatic prostate cancer but not in primary disease, it does not consistently predict differential responses to androgen receptor signaling inhibitors or PARP inhibitors in the studied datasets.